Abstract: Additive manufacturing (AM, a.k.a. 3D printing) enables individualized manufacturing of low-volume products with huge varieties and geometric complexity. Control of 3D shape deformation in AM built products has been a challenging issue, particularly under a Cyber-Physical AM environment with diverse fabrication conditions. This talks presents a smart and adaptable accuracy control methodology which entails prescriptive modeling of shape deformation based on limited test shapes, optimal compensation of shape deformation through a close-form solution; Bayesian learning of disparate AM data, transfer learning between different AM process conditions.

About Speaker: Prof. Qiang Huang received his Ph.D. degree in Industrial and Operations Engineering from the University of Michigan-Ann Arbor. He is currently an Associate Professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. His research interests include Integrated Nanomanufacturing & Nanoinformatics and Foundations of Quality Control for Additive Manufacturing. He was the holder of Gordon S. Marshall Early Career Chair in Engineering at USC from 2012 to 2016. He received National Science Foundation CAREER award in 2011 and IEEE Transactions on Automation Science and Engineering Best Paper Award from IEEE Robotics and Automation Society in 2014. He is a member of IEEE, INFORMS, ASME and IIE.